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Motivation is a concept which has generated a substantial amount of research. This concept is of significant interest given that it can provide useful insights into understanding the underlying drive of individuals' actions and behaviour. When examining theories of motivation, some there are a number of them which stand out. One of them is obviously Arousal Theory. This theory postulates that people tend to act out certain types of behaviour as means of increasing or decreasing their levels of arousal (Weiner, 2013). In this way, one should note that one of the key theoretical assumptions of basic assumptions of Arousal Theory is the fact that environmental factors may influence the brain's level of arousal (Peters, 2015). A considerable amount of research and scholarship has demonstrated that Arousal Theory is a useful framework to explaining the ways in which individuals undergo different levels of arousal brought about through a variety of experiences (Deckers, 2015). It follows that, when levels of arousal are low, individuals may feel bored and thus engage in activities which will increase their arousal levels (Strombach, Strang, Park & Kenning, 2016). Similarly, when arousal levels are too high, such as in the case of being very anxious and overstressed, one my resort to engaging in relaxation methods (this may involve meditating, reading a book, or getting a massage [Deckers, 2015]).

It has been postulated that maintenance of an optimal level of arousal is the key for providing the main motivation for the individual (Corbett, Swain, Newsom, Wang, Song & Edgerton, 2014). However, one should note that optimal levels of arousal may vary from one individual to another as some people may be natural thrill seekers and would require intense emotional, physical, and intellectual activities to make them feel happy (Strombach, Strang, Park & Kenning, 2016). In contrast, other people may prefer low levels of arousal and engage in activities such as taking an afternoon nap, watching TV, or reading a book (Peters, 2015). In this way, Arousal Theory proposes that, ultimately, individuals seek to achieve an optimum level of arousal in order to perform to our best capacity (Peters, 2015).

Inverted U Theory

Another prominent theory of arousal is commonly known as inverted U theory, and was developed by Yerkes and Dodson in 1908 (Kerr, 2014). Similar to Arousal Theory, this theory also holds that optimal performance occurs when the performer reaches an optimal level of arousal (Kerr, 2014). More specifically, it would appear that individuals will improve their performance as arousal increases until it suitably reaches a point in which optimum performance has been reached, whereby arousal is at its optimal level (Kerr, 2014). In addition, one should also note that when arousal increases beyond the point of optimum level, performance begins to deteriorate (Shih & Lin, 2016). However, this theory attracted a number of criticisms from some scholars who argued that this theory fits into observations from sports performers, but in fact it may be too simplistic to adapt to the understanding of whether or not U theory applies equally to expert performers and beginners (Shih & Lin, 2016). Furthermore, one should also note that levels of arousal may also vary according to the skill set required to perform a given task (Shih & Lin, 2016). For instance, in sports, performers who engage in activities which incorporate major muscle groups may benefit from higher levels of arousal (Sapolsky, 2015). In contrast, performers that involved in activities which are low physical demanding, and only involve finer skills (e.g. snooker, darts), may benefit from lower levels of arousal (Sapolsky, 2015).

However, it is thought that one of the major problems with the inverted-U hypothesis as an explanation for the relationship between arousal and performance is the way in which activation and arousal are operationalised (Sapolsky, 2015). For instance, previous studies (see Duffy, 1962) arousal has been regarded as a unidimensional activation response which prepares individuals for action. This view was also shared by Malmo (1959) who viewed arousal as lying on a continuum from deep sleep to extreme excitement. Thus, from this vantage point, arousal can be conceptualised as a unitary construct which accounts for a wide range of cognitive, physiological, and behavioural factors (Shih & Lin, 2016).

The Relationship Between Arousal and Working Memory

It is well established that moderate fluctuations in feelings and emotions can systematically distort cognitive processing (Isen, 1993; Ashby, Isen & Turken, 1999). For instance, it has been demonstrated that even mild forms of positive affect can improve creative problem solving (Isen, Daubman, & Nowicki, 1987). It is thought that environmental conditions which induce positive effect are just as likely to increase arousal (see Zillmann, 1979; LeDoux, 1996). This issue may raise the important question as to what is the difference between arousal and affect. In this respect, it has been argued that there are two common ways of manipulating arousal including; (1) through inducing an emotional state (e.g. anger or fear [LeDoux, 1996]), and (2) through exercise (Zillmann, 1979). The present study seeks to explore and gain further insights into how arousal may affect cognitive performance during tasks which require working memory.



Participants (N=37, female=31, male=6) were randomly sampled from a population of university students, aged between 18-48 (M=23.43, SD=6.9). Participants belonged to a non-clinical population, and had normal, or corrected to normal vision.


Three conditions (i.e. silence, low intensity white noise, high intensity white noise) were manipulated within subjects. Participants randomly presented with all 3 conditions throughout the main study.


It has been hypothesised in this study that:

H1 - The level of white noise has an impact on working memory

Independent variable: Level of white noise

Dependent variable: Number of correct responses on working memory task


Informed consent was obtained before beginning the study. Prior to starting the experiment, participants were provided with clear and precise information concerning the nature of the study. For the purpose of this experiment, respondents were required to engage on a behavioural task. A list of digits between 1 and 6 were presented followed by a tone. After the tone they were shown another digit in red. Their main task was to determine whether the digit they saw was present in the list shown prior to the tone. They were instructed to respond as fast and as accurately as possible. Answers were registered by pressing keyboard keys "1" and "2" for Yes, and No respectively.

Statistical Procedure

Data collected as part of this experiment was analysed using IBM SPSS Statistical Software Package (Version 22). The relationship between independent and dependent variable was determined through means of ANOVA tests.


Presentation of results was divided into two components including descriptive statistics, and inferential statistics. Normality tests were also carried out in order to examine if collected data conformed with key prerequisites and assumptions for parametric statistical tests. It is also worth mentioning that Criterion [alpha] (alpha) level for statistical significance of relevant tests was set at p[less than or equal to] 0.05 for ANOVA computations.

Descriptive Statistics

Table 1 shows descriptive statistics concerning participants' demographics, as well as scores relating to Galvanic Skin Responses and Heart Rate. Galvanic Skin Responses (GSR) were higher for men in all three conditions (Silent: M=18.77; SD=18.46; Low: M=22.95; SD=21.58; High: M=11.27; SD=6.19) compared to their female counterparts (Silent: M=4.44; SD=6.76; Low: M=5.21; SD=5.60; High: M=5.47; SD=7.23). Similarly, Heart Rate was lower among female participants across all conditions (Silent: M=79.70; SD=10.47; Low: M=84.06; SD=19.79; High: M=85.09; SD=20.63) compared to male participants (Silent: M=83.97; SD=13.75; Low: M=84.32; SD=14.67; High: M=72.49; SD=35.57).

As prescribed by Kim (2013), when the values of skewness and kurtosis are between -1.96 and 1.96 the assumption of normality can be accepted. Initial tests (i.e. Skewness and Kurtosis) were carried out in order to find out if average response times, accuracy, and confidence scores satisfied prescribed assumptions required for parametric tests. The above table shows that satisfied this assumption as skewness and kurtosis values are between -1.96 and 1.96.

Measure for white noise - GSR

Mauchly's test indicated that the assumption of sphericity had been violated, [X.sup.2](2) = 37.83, p <.001, therefore Greenhouse-Geisser corrected tests are reported. The results show that the level of white noise GSR had a significant effect on Working Memory., F (1.20, 43.36) = .89, p < .001.

Measure of white noise--HR (heart rate)

A further analysis was made using ANOVA, to measure the level of heart rate: Mauchly's test indicated that the assumption of sphericity had been violated, [X.sup.2](2) = 13.97, p = .001, therefore Greenhouse-Geisser corrected tests are reported. The results show that the level of white noise HR had a significant effect on Working Memory, F(1.51, 54.18) = 2.21, p = .001.


Present results indicate that is a relationship between working memory and arousal. This pattern of results strongly supports the hypothesis that level of white noise has an impact on working memory (H1). The present findings have some important implications in the field of competitive sports. This particularly due to the fact that competitive sporting events can be moment of intense emotional upheaval for athletes. The pressures to compete at an elite level may have the effect of yielding either outstanding or poor performances. Thus, when using Inverted U Theory as a framework one could argue that in the case of athletes they must be aroused just at optimal level in order to perform at their best.


In conclusion, it should be evident that arousal plays a crucial role in determining optimal levels of cognitive performance during tasks which require working memory. Present findings have important implications for sports and improving athlete's performance by finding optimal levels of arousal. It is hoped that the present study will act as a catalyst for future and more research connecting working memory and arousal research. Future studies should adopt neural imaging techniques such as Electroencephalogram (EEG) and functional Magnetic Resonance Imaging (fMRI) in order to gain further insights into understanding the neural correlates of arousal during tasks involving working memory.


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Table 1--Participant Demographics

            Women          Men
            (N=31)         (N=6)
            M (SD)         M (SD)

Age          23.13 (5.92)     25 (11.35)
Silent GSR    4.44 (6.76)  18.77 (18.46)
Low GSR       5.21 (5.60)  22.95 (21.58)
High GSR      5.47 (7.23)   11.27 (6.19)
Silent HR   79.70 (10.47)  83.97 (13.75)
Low HR      84.06 (19.79)  84.32 (14.67)
High HR     85.09 (20.63)  72.49 (35.57)
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Article Details
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Author:Polledri, Harry
Publication:Journal of Social and Psychological Sciences
Article Type:Report
Date:Jan 1, 2017

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